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Towards fostering growth mindset classrooms: identifying teaching behaviors that signal instructors’ fixed and growth mindsets beliefs to students

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Abstract

Students who perceive their instructors to endorse growth (vs. fixed) mindset beliefs report better classroom experiences (e.g., greater belonging, fewer evaluative concerns) and, in turn, engage in more behaviors that promote academic success (e.g., class attendance and engagement). Although many instructors personally endorse growth (vs. fixed) mindset beliefs, their students often perceive their beliefs quite differently. And, to date, little is known about how students come to perceive their instructors as growth-minded or as fixed-minded. To address this, the present research employs a social cognitive classification paradigm to identify teaching behaviors that students perceive as communicating instructors’ mindset beliefs. College students (NStudents = 186) categorized specific teaching behaviors (NBehaviors = 119) as signaling either fixed or growth mindset beliefs. Even after controlling for students’ personal mindset beliefs and the warmth of the teaching behavior, we found that when instructors suggest everyone can learn, offer opportunities for feedback, respond to struggling students with additional support and attention, and place value on learning it signals to students that their instructor endorses more growth mindset beliefs. Conversely, when instructors suggest that some students are incapable, fail to provide opportunities for feedback, respond to students’ struggle with frustration and/or resignation, and place value on performance and brilliance it signals to students that their instructor endorses fixed mindset beliefs.

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Data availability

The de-identified dataset, codebook, and method file are publicly available on the Open Science Framework (OSF) website (https://osf.io/3jxn4/?view_only=3937dbae146e4e37a2178a9cfda977a0).

Notes

  1. Our original predictions regarding how most students would categorize cues and our data exclusion plans were preregistered on the Open Science Framework prior to data processing and analysis (see https://osf.io/d28aq/?view_only=85cc3df98bc24a92b5fe8eacfa701c39). Overall, we found that 92 of the 119 cues (77.3%) were categorized by the majority of students as predicted. Refer to the Supplementary Information for more details regarding the research team’s a priori predictions.

  2. Two-hundred participants were initially recruited to participate in this study. Twelve students were excluded from all analyses for taking over 15 min to complete the learning module and, therefore, not having enough time to complete the categorization task within the 30-min study timeframe. Additionally, two students were dropped from the study for failing to complete the personal beliefs survey. As a result, our final sample includes the 186 students who fully completed the study. All participants, regardless of study completion, earned course credit.

  3. After reading the task instructions, students completed a short 4-item quiz designed to test their comprehension of the instructions (e.g., “Which of the following statements describes a GROWTH mindset?”; Correct answer: “Human traits, like intelligence, can be changed or improved”). Students needed to answer all 4 questions correctly before they could move onto the categorization task. If students answered any questions incorrectly, they were asked to re-read the task instructions and try again. Most participants finished reading the task instructions and completing the comprehension quiz in a single attempt (93.0%) and within three minutes of starting the study (M = 2.19 min, SD = 0.37). The task instructions, comprehension quiz questions, and answers are provided in the Supplementary Information.

  4. As students categorized the various teaching behaviors, we tracked their computer mouse movements using MouseTracker software (v. 2.84; Freeman & Ambady, 2010). Mouse tracking software is typically used to identify decision conflict—or uncertainty in decision-making. We used mouse tracking software with the intention of exploring the teaching behaviors that students had the most and least difficulty categorizing. Multilevel analyses, however, revealed very little variability in decision conflict at the Behavior-Level (ICC = .02). Instead, most variability in decision conflict occurred at the Student-Level (ICC = .30). This suggests that most behaviors were similarly easy (or difficult) for students to categorize; instead, the variability that we observed suggests that some students simply had more difficulty (or ease) categorizing behaviors overall compared to other students. We provide the full mouse tracking analyses with all the decision uncertainty indicators for interested readers in the Supplementary Information.

  5. Students were asked to sort stimuli as quickly as possible. This instruction is in line with best practices using mouse-tracking software, and it is meant to ensure that students’ mouse trajectories reflect real time mental processing of cues (Freeman & Ambady, 2010; Kieslich et al., 2019).

  6. Category label ordering had no detectable effect on students’ categorization decisions (p = .465).

  7. See the Supplementary Information for full survey measures.

  8. See the Supplementary Information for the teaching behavior themes codebook.

  9. Interrater reliabilities were high (all average measures ICCs > 0.80). All disagreements were resolved through discussion. See the Supplementary Information for further details about coding and interrater reliability.

  10. Intraclass Correlation Coefficients were estimated in R version 4.0.2, using the ICCBin package (Hossain & Chakraborty, 2017), because the outcome variable is binary. We adopted the Chakraborty and Sen (2016) resampling method for estimation of the ICC and its confidence interval, due to its increased estimation precision over other approximation methods.

  11. All multilevel analyses were conducted in R version 4.0.2, using the lme4 package (Bates et al., 2015).

  12. Prior to running this model, we examined whether the multilevel logistic regression model improved model fit over a standard logistic regression model. Initially, we estimated an empty logit model, that lacked fixed and random effects, with categorization decision as the binary outcome variable. In a second model we added random effects for Behavior and for Student. Then we compared model fit (see Table 4). The second model offered a clearly superior fit over the first model (AICDifference = 18,735.71), so we proceeded with the multilevel logistic regression model for the focal analysis.

  13. Effects coding was used to examine the role of the teaching behavior themes on categorization decisions: + 1 “growth-signaling”, − 1 “fixed-signaling”.

  14. Each theme was also entered and examined independently, resulting in similar conclusions. See the Supplementary Information for these analyses.

  15. Analyses without covariates are provided in the Supplementary Information for interested readers.

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Acknowledgements

We thank our undergraduate research assistants who assisted with data collection: Brody McKee, Alissa Rumsey, Izabella Spriggs, Caroline Toland, Sydney Whiteford, and Wendy Wu. And for feedback supportive of this research, we thank members of the Mind and Identity in Context Lab at Indiana University (alphabetized): Tessa Benson-Greenwald, Elizabeth Canning, Rylan Deer, Trisha Dehrone, Amanda Diekman, Tiffany Estep, Dorainne Green, Caitlyn Jones, Mansi Joshi, Jennifer LaCosse, Elinam Ladzekpo, Katherine Muenks, Elise Ozier, Stephanie Reeves, Tennisha Riley, Apoorva Sarmal, and Nedim Yel; as well as members of the Motivation and Cognitive Science Lab at The Ohio State University (alphabetized): Taylor Ballinger, Michael Diamond, Kentaro Fujita, Tao Jiang, Phuong Le, Seulbee Lee, Allison Londerée, and Tina Nguyen.

Funding

This research was funded in part by NSF grants (DRL-1450755 and HRD-1661004) awarded to Mary C. Murphy.

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Author notes

  1. Audrey C. Fried and Kathryn M. Kroeper share first authorship as their contributions and leadership of this manuscript were shared.

Authors and Affiliations

  1. Department of Psychology, The Ohio State University, 1827 Neil Avenue, Columbus, OH, 43210, USA

    Kathryn M. Kroeper

  2. Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA

    Audrey C. Fried & Mary C. Murphy

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Contributions

First authorship between K. M. Kroeper and A.C. Fried is shared. All authors jointly developed the study concept, experimental design, and study materials. M.C. Murphy obtained the grant that supported the creation of the study materials, data collection, and analysis. Data collection was performed by A.C. Fried, under the supervision of K. M. Kroeper. Data analysis and interpretation was performed by K. M. Kroeper and A.C. Fried. The initial version of the manuscript was drafted by A.C. Fried (for her undergraduate honors thesis). K. M. Kroeper revised the manuscript for publication. All authors provided revisions and feedback to the manuscript and approved the final version of the manuscript for submission.

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Correspondence to Kathryn M. Kroeper or Mary C. Murphy.

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Kroeper, K.M., Fried, A.C. & Murphy, M.C. Towards fostering growth mindset classrooms: identifying teaching behaviors that signal instructors’ fixed and growth mindsets beliefs to students. Soc Psychol Educ 25, 371–398 (2022). https://doi.org/10.1007/s11218-022-09689-4

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